
import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 			UpperCAmelCase_     (					_A    ,			unittest.TestCase	):


  '''simple docstring'''







  a__       			=						AudioLDMPipeline
  a__       			=						TEXT_TO_AUDIO_PARAMS
  a__       			=						TEXT_TO_AUDIO_BATCH_PARAMS
  a__       			=						frozenset(
      [
          """num_inference_steps""",
          """num_waveforms_per_prompt""",
          """generator""",
          """latents""",
          """output_type""",
          """return_dict""",
          """callback""",
          """callback_steps""",
      ]	)
  def 				_lowercase				(					self     :					List[Any]       )     ->      List[str]:







    """simple docstring"""






    torch.manual_seed(0       )
    __magic_name__								=			UNetaDConditionModel(
        block_out_channels=(32, 64)     ,				layers_per_block=2     ,				sample_size=32     ,				in_channels=4     ,				out_channels=4     ,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")     ,				up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")     ,				cross_attention_dim=(32, 64)     ,				class_embed_type="""simple_projection"""     ,				projection_class_embeddings_input_dim=32     ,				class_embeddings_concat=UpperCamelCase__     ,				)
    __magic_name__								=			DDIMScheduler(
        beta_start=0.00085     ,				beta_end=0.012     ,				beta_schedule="""scaled_linear"""     ,				clip_sample=UpperCamelCase__     ,				set_alpha_to_one=UpperCamelCase__     ,				)
    torch.manual_seed(0       )
    __magic_name__								=			AutoencoderKL(
        block_out_channels=[32, 64]     ,				in_channels=1     ,				out_channels=1     ,				down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]     ,				up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]     ,				latent_channels=4     ,				)
    torch.manual_seed(0       )
    __magic_name__								=			ClapTextConfig(
        bos_token_id=0     ,				eos_token_id=2     ,				hidden_size=32     ,				intermediate_size=37     ,				layer_norm_eps=1E-05     ,				num_attention_heads=4     ,				num_hidden_layers=5     ,				pad_token_id=1     ,				vocab_size=1000     ,				projection_dim=32     ,				)
    __magic_name__								=			ClapTextModelWithProjection(UpperCamelCase__       )
    __magic_name__								=			RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta"""     ,				model_max_length=77       )

    __magic_name__								=			SpeechTaHifiGanConfig(
        model_in_dim=8     ,				sampling_rate=1_6000     ,				upsample_initial_channel=16     ,				upsample_rates=[2, 2]     ,				upsample_kernel_sizes=[4, 4]     ,				resblock_kernel_sizes=[3, 7]     ,				resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]     ,				normalize_before=UpperCamelCase__     ,				)

    __magic_name__								=			SpeechTaHifiGan(UpperCamelCase__       )

    __magic_name__								=			{
        """unet""": unet,
        """scheduler""": scheduler,
        """vae""": vae,
        """text_encoder""": text_encoder,
        """tokenizer""": tokenizer,
        """vocoder""": vocoder,
    }
    return components
  def 				_lowercase				(					self     :					Optional[int]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					List[Any]=0       )     ->      Optional[Any]:







    """simple docstring"""






    if str(UpperCamelCase__       ).startswith("""mps"""       ):
      __magic_name__								=			torch.manual_seed(UpperCamelCase__       )
    else:
      __magic_name__								=			torch.Generator(device=UpperCamelCase__       ).manual_seed(UpperCamelCase__       )
    __magic_name__								=			{
        """prompt""": """A hammer hitting a wooden surface""",
        """generator""": generator,
        """num_inference_steps""": 2,
        """guidance_scale""": 6.0,
    }
    return inputs
  def 				_lowercase				(					self     :					Tuple       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			"""cpu"""  # ensure determinism for the device-dependent torch.Generator

    __magic_name__								=			self.get_dummy_components()
    __magic_name__								=			AudioLDMPipeline(**UpperCamelCase__       )
    __magic_name__								=			audioldm_pipe.to(UpperCamelCase__       )
    audioldm_pipe.set_progress_bar_config(disable=UpperCamelCase__       )

    __magic_name__								=			self.get_dummy_inputs(UpperCamelCase__       )
    __magic_name__								=			audioldm_pipe(**UpperCamelCase__       )
    __magic_name__								=			output.audios[0]

    assert audio.ndim == 1
    assert len(UpperCamelCase__       ) == 256

    __magic_name__								=			audio[:10]
    __magic_name__								=			np.array(
        [-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033]       )

    assert np.abs(audio_slice - expected_slice       ).max() < 1E-2
  def 				_lowercase				(					self     :					List[str]       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			self.get_dummy_components()
    __magic_name__								=			AudioLDMPipeline(**UpperCamelCase__       )
    __magic_name__								=			audioldm_pipe.to(UpperCamelCase__       )
    __magic_name__								=			audioldm_pipe.to(UpperCamelCase__       )
    audioldm_pipe.set_progress_bar_config(disable=UpperCamelCase__       )

    __magic_name__								=			self.get_dummy_inputs(UpperCamelCase__       )
    __magic_name__								=			3 * [inputs["""prompt"""]]

    # forward
    __magic_name__								=			audioldm_pipe(**UpperCamelCase__       )
    __magic_name__								=			output.audios[0]

    __magic_name__								=			self.get_dummy_inputs(UpperCamelCase__       )
    __magic_name__								=			3 * [inputs.pop("""prompt"""       )]

    __magic_name__								=			audioldm_pipe.tokenizer(
        UpperCamelCase__     ,				padding="""max_length"""     ,				max_length=audioldm_pipe.tokenizer.model_max_length     ,				truncation=UpperCamelCase__     ,				return_tensors="""pt"""     ,				)
    __magic_name__								=			text_inputs["""input_ids"""].to(UpperCamelCase__       )

    __magic_name__								=			audioldm_pipe.text_encoder(
        UpperCamelCase__     ,				)
    __magic_name__								=			prompt_embeds.text_embeds
    # additional L_2 normalization over each hidden-state
    __magic_name__								=			F.normalize(UpperCamelCase__     ,				dim=-1       )

    __magic_name__								=			prompt_embeds

    # forward
    __magic_name__								=			audioldm_pipe(**UpperCamelCase__       )
    __magic_name__								=			output.audios[0]

    assert np.abs(audio_a - audio_a       ).max() < 1E-2
  def 				_lowercase				(					self     :					int       )     ->      str:







    """simple docstring"""






    __magic_name__								=			self.get_dummy_components()
    __magic_name__								=			AudioLDMPipeline(**UpperCamelCase__       )
    __magic_name__								=			audioldm_pipe.to(UpperCamelCase__       )
    __magic_name__								=			audioldm_pipe.to(UpperCamelCase__       )
    audioldm_pipe.set_progress_bar_config(disable=UpperCamelCase__       )

    __magic_name__								=			self.get_dummy_inputs(UpperCamelCase__       )
    __magic_name__								=			3 * ["""this is a negative prompt"""]
    __magic_name__								=			negative_prompt
    __magic_name__								=			3 * [inputs["""prompt"""]]

    # forward
    __magic_name__								=			audioldm_pipe(**UpperCamelCase__       )
    __magic_name__								=			output.audios[0]

    __magic_name__								=			self.get_dummy_inputs(UpperCamelCase__       )
    __magic_name__								=			3 * [inputs.pop("""prompt"""       )]

    __magic_name__								=			[]
    for p in [prompt, negative_prompt]:
      __magic_name__								=			audioldm_pipe.tokenizer(
          UpperCamelCase__     ,				padding="""max_length"""     ,				max_length=audioldm_pipe.tokenizer.model_max_length     ,				truncation=UpperCamelCase__     ,				return_tensors="""pt"""     ,				)
      __magic_name__								=			text_inputs["""input_ids"""].to(UpperCamelCase__       )

      __magic_name__								=			audioldm_pipe.text_encoder(
          UpperCamelCase__     ,				)
      __magic_name__								=			text_embeds.text_embeds
      # additional L_2 normalization over each hidden-state
      __magic_name__								=			F.normalize(UpperCamelCase__     ,				dim=-1       )

      embeds.append(UpperCamelCase__       )

    __magic_name__	,       __magic_name__								=			embeds

    # forward
    __magic_name__								=			audioldm_pipe(**UpperCamelCase__       )
    __magic_name__								=			output.audios[0]

    assert np.abs(audio_a - audio_a       ).max() < 1E-2
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Optional[int]:







    """simple docstring"""






    __magic_name__								=			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
    __magic_name__								=			self.get_dummy_components()
    __magic_name__								=			PNDMScheduler(skip_prk_steps=UpperCamelCase__       )
    __magic_name__								=			AudioLDMPipeline(**UpperCamelCase__       )
    __magic_name__								=			audioldm_pipe.to(UpperCamelCase__       )
    audioldm_pipe.set_progress_bar_config(disable=UpperCamelCase__       )

    __magic_name__								=			self.get_dummy_inputs(UpperCamelCase__       )
    __magic_name__								=			"""egg cracking"""
    __magic_name__								=			audioldm_pipe(**UpperCamelCase__     ,				negative_prompt=UpperCamelCase__       )
    __magic_name__								=			output.audios[0]

    assert audio.ndim == 1
    assert len(UpperCamelCase__       ) == 256

    __magic_name__								=			audio[:10]
    __magic_name__								=			np.array(
        [-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032]       )

    assert np.abs(audio_slice - expected_slice       ).max() < 1E-2
  def 				_lowercase				(					self     :					Any       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
    __magic_name__								=			self.get_dummy_components()
    __magic_name__								=			PNDMScheduler(skip_prk_steps=UpperCamelCase__       )
    __magic_name__								=			AudioLDMPipeline(**UpperCamelCase__       )
    __magic_name__								=			audioldm_pipe.to(UpperCamelCase__       )
    audioldm_pipe.set_progress_bar_config(disable=UpperCamelCase__       )

    __magic_name__								=			"""A hammer hitting a wooden surface"""

    # test num_waveforms_per_prompt=1 (default)
    __magic_name__								=			audioldm_pipe(UpperCamelCase__     ,				num_inference_steps=2       ).audios

    assert audios.shape == (1, 256)

    # test num_waveforms_per_prompt=1 (default) for batch of prompts
    __magic_name__								=			2
    __magic_name__								=			audioldm_pipe([prompt] * batch_size     ,				num_inference_steps=2       ).audios

    assert audios.shape == (batch_size, 256)

    # test num_waveforms_per_prompt for single prompt
    __magic_name__								=			2
    __magic_name__								=			audioldm_pipe(UpperCamelCase__     ,				num_inference_steps=2     ,				num_waveforms_per_prompt=UpperCamelCase__       ).audios

    assert audios.shape == (num_waveforms_per_prompt, 256)

    # test num_waveforms_per_prompt for batch of prompts
    __magic_name__								=			2
    __magic_name__								=			audioldm_pipe(
        [prompt] * batch_size     ,				num_inference_steps=2     ,				num_waveforms_per_prompt=UpperCamelCase__       ).audios

    assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
  def 				_lowercase				(					self     :					int       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
    __magic_name__								=			self.get_dummy_components()
    __magic_name__								=			AudioLDMPipeline(**UpperCamelCase__       )
    __magic_name__								=			audioldm_pipe.to(UpperCamelCase__       )
    audioldm_pipe.set_progress_bar_config(disable=UpperCamelCase__       )
    __magic_name__								=			audioldm_pipe.vocoder.config.sampling_rate

    __magic_name__								=			self.get_dummy_inputs(UpperCamelCase__       )
    __magic_name__								=			audioldm_pipe(audio_length_in_s=0.016     ,				**UpperCamelCase__       )
    __magic_name__								=			output.audios[0]

    assert audio.ndim == 1
    assert len(UpperCamelCase__       ) / vocoder_sampling_rate == 0.016

    __magic_name__								=			audioldm_pipe(audio_length_in_s=0.032     ,				**UpperCamelCase__       )
    __magic_name__								=			output.audios[0]

    assert audio.ndim == 1
    assert len(UpperCamelCase__       ) / vocoder_sampling_rate == 0.032
  def 				_lowercase				(					self     :					Any       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.get_dummy_components()
    __magic_name__								=			AudioLDMPipeline(**UpperCamelCase__       )
    __magic_name__								=			audioldm_pipe.to(UpperCamelCase__       )
    audioldm_pipe.set_progress_bar_config(disable=UpperCamelCase__       )

    __magic_name__								=			["""hey"""]

    __magic_name__								=			audioldm_pipe(UpperCamelCase__     ,				num_inference_steps=1       )
    __magic_name__								=			output.audios.shape
    assert audio_shape == (1, 256)

    __magic_name__								=			audioldm_pipe.vocoder.config
    config.model_in_dim *= 2
    __magic_name__								=			SpeechTaHifiGan(UpperCamelCase__       ).to(UpperCamelCase__       )
    __magic_name__								=			audioldm_pipe(UpperCamelCase__     ,				num_inference_steps=1       )
    __magic_name__								=			output.audios.shape
    # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
    assert audio_shape == (1, 256)
  def 				_lowercase				(					self     :					Tuple       )     ->      Dict:







    """simple docstring"""






    self._test_attention_slicing_forward_pass(test_mean_pixel_difference=UpperCamelCase__       )
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      List[str]:







    """simple docstring"""






    self._test_inference_batch_single_identical(test_mean_pixel_difference=UpperCamelCase__       )





  @unittest.skipIf(
      torch_device != """cuda""" or not is_xformers_available()     ,				reason="""XFormers attention is only available with CUDA and `xformers` installed"""     ,				)
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Optional[Any]:







    """simple docstring"""






    self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=UpperCamelCase__       )







@slow
class 			UpperCAmelCase_     (					unittest.TestCase	):


  '''simple docstring'''







  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Any:







    """simple docstring"""






    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Any="cpu"     ,				UpperCamelCase__     :					Optional[Any]=torch.floataa     ,				UpperCamelCase__     :					str=0       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			torch.Generator(device=UpperCamelCase__       ).manual_seed(UpperCamelCase__       )
    __magic_name__								=			np.random.RandomState(UpperCamelCase__       ).standard_normal((1, 8, 128, 16)       )
    __magic_name__								=			torch.from_numpy(UpperCamelCase__       ).to(device=UpperCamelCase__     ,				dtype=UpperCamelCase__       )
    __magic_name__								=			{
        """prompt""": """A hammer hitting a wooden surface""",
        """latents""": latents,
        """generator""": generator,
        """num_inference_steps""": 3,
        """guidance_scale""": 2.5,
    }
    return inputs
  def 				_lowercase				(					self     :					Any       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""       )
    __magic_name__								=			audioldm_pipe.to(UpperCamelCase__       )
    audioldm_pipe.set_progress_bar_config(disable=UpperCamelCase__       )

    __magic_name__								=			self.get_inputs(UpperCamelCase__       )
    __magic_name__								=			25
    __magic_name__								=			audioldm_pipe(**UpperCamelCase__       ).audios[0]

    assert audio.ndim == 1
    assert len(UpperCamelCase__       ) == 8_1920

    __magic_name__								=			audio[7_7230:7_7240]
    __magic_name__								=			np.array(
        [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]       )
    __magic_name__								=			np.abs(expected_slice - audio_slice       ).max()
    assert max_diff < 1E-2





  def 				_lowercase				(					self     :					List[str]       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""       )
    __magic_name__								=			LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config       )
    __magic_name__								=			audioldm_pipe.to(UpperCamelCase__       )
    audioldm_pipe.set_progress_bar_config(disable=UpperCamelCase__       )

    __magic_name__								=			self.get_inputs(UpperCamelCase__       )
    __magic_name__								=			audioldm_pipe(**UpperCamelCase__       ).audios[0]

    assert audio.ndim == 1
    assert len(UpperCamelCase__       ) == 8_1920

    __magic_name__								=			audio[2_7780:2_7790]
    __magic_name__								=			np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]       )
    __magic_name__								=			np.abs(expected_slice - audio_slice       ).max()
    assert max_diff < 3E-2



import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def a__       (			A_,	A_,	A_   ):






  '''simple docstring'''





  if gpta_config_file == "":
    __magic_name__								=			GPTaConfig()
  else:
    __magic_name__								=			GPTaConfig.from_json_file(A_   )
  __magic_name__								=			GPTaModel(A_   )

  # Load weights from numpy
  load_tf_weights_in_gpta(A_,	A_,	A_   )

  # Save pytorch-model
  __magic_name__								=			pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
  __magic_name__								=			pytorch_dump_folder_path + """/""" + CONFIG_NAME
  print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''   )
  torch.save(model.state_dict(),	A_   )
  print(f'''Save configuration file to {pytorch_config_dump_path}'''   )
  with open(A_,	"""w""",	encoding="""utf-8"""   ) as f:
    f.write(config.to_json_string()   )


if __name__ == "__main__":
    __lowerCAmelCase     :	List[str]       			=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    parser.add_argument(
        '--gpt2_config_file',
        default='',
        type=str,
        help=(
            'An optional config json file corresponding to the pre-trained OpenAI model. \n'
            'This specifies the model architecture.'
        ),
    )
    __lowerCAmelCase     :	Tuple       			=  parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)

